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                《npj 计算材料学》是在线出版、完全開放獲取的国际学术期刊。发表结合计算模拟与设计的材料学一流的研究成果。本刊由中國科學院上海矽酸鹽研究所与英国自然出版集团(Nature Publishing Group,NPG)以伙伴关系合作出版。
                主編爲陳龍慶博士,美國賓州大學材料科學與工程系、工程科學與力學系、數學系的傑出教授。
                共同主编为陈立东研究员,中國科學院上海矽酸鹽研究所研究员高性能陶瓷与超微结构国家重点实验室主任。
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              3D non-isothermal phase-field simulation of microstructure evolution during selective laser sintering (选区激光烧结微结构演化的三维非等温相场模拟)
              Yangyiwei Yang, Olav Ragnvaldsen, Yang Bai, Min Yi & Bai-Xiang Xu
              npj Computational Materials 5:81(2019)
              doi:s41524-019-0219-7
              Published online:06 August 2019

              Abstract| Full Text | PDF OPEN

              摘要:選區激光燒結(SLS)增材制造過程中,微結構演化極度依賴于局部溫度的急劇變化,故而常規的等溫相場模型很難適用于SLS的模擬。本研究報道了一種新的非等溫相場模型,該模型從熵出發,熱力學自洽地推導出了控制微結構序參量演化的非等溫動力學方程,以及耦合微結構演化的熱傳導方程,並考慮了SLS局部極高溫導致的局部熔化以及激光-粉末相互作用。該模型經三維有限元數值化後,被用于模擬單次掃描的SLS。爲了減小計算量並加快計算速度,提出了一種類似于求解最小著色數問題的新算法,結合晶粒追蹤方法,該算法可僅用8個序參量來模擬具有多達200個晶粒的系統。特別地,將該非等溫相場模型用于SLS處理316L不鏽鋼粉末的研究,揭示了激光功率和掃描速度對孔隙率、表面形貌、溫度分布、晶粒幾何形狀以及致密度等微觀結構特征的影響規律。此外,模擬結果驗證了致密化過程中孔隙率變化的一階動力學特征,並證實了該模型可用于預測SLS過程中致密化因子與激光比能量之間的關聯   

              Abstract:During selective laser sintering (SLS), the microstructure evolution and local temperature variation interact mutually. Application of conventional isothermal sintering model is thereby insufficient to describe SLS. In this work, we construct our model from entropy level, andderive the non-isothermal kinetics for order parameters along with the heat transfer equation coupled with microstructure evolution. Influences from partial melting and laser-powder interaction are also addressed. We then perform 3D finite element non-isothermal phase-field simulations of the SLS single scan. To confront the high computation cost, we propose a novel algorithm analogy to minimum coloring problem and manage to simulate a system of 200 grains with grain tracking algorithm using as low as 8 non-conserved order parameters. Specifically, applying the model to SLS of the stainless steel 316L powder, we identify the influences of laser power and scan speed on microstructural features, including the porosity, surface morphology, temperature profile, grain geometry, and densification. We further validate the first-order kinetics of the transient porosity during densification, and demonstrate the applicability of the developed model in predicting the linkage of densification factor to the specific energy input during SLS. 

              Editorial Summary

              Additive Manufacturing: Sino-German cooperation predicts complex microstructures增材制造:中德合作預測複雜微結構

              該研究提出了一种热力学自洽的非等温相场模型以及相应的三维高效數值方法,可以模拟選區激光燒結(SLS)增材制造中複雜微結構的演化過程。德國達姆施塔特工業大學的終身教授胥柏香領導的團隊,與南京航空航天大學的青年千人易敏教授合作,報道了一種熱力學自洽的非等溫相場模型,考慮微結構與熱傳導的強耦合、SLS局部極高溫導致的局部熔化以及激光-粉末相互作用。他們提出了一種類似于求解最小著色數問題的新解決方案,結合晶粒追蹤方法,該方案可僅用8個序參量來模擬具有多達200個晶粒的系統。研究人員還使用了基于LM算法的非線性優化方法,同時擬合模型與實驗中表面能、晶界能隨溫度變化的趨勢,以獲取用于非恒溫相場的模型參數。特別地,將該非等溫相場模型用于SLS處理316L不鏽鋼粉末的研究,揭示了激光功率和掃描速度對孔隙率、表面形貌、溫度分布、晶粒幾何形狀以及致密度等微觀結構特征的影響規律,並證實了該模型可用于預測SLS過程中致密化因子與激光比能量之間的關聯。他们的研究为基于SLS的增材制造的建模及計算模擬提供了有效方法或工具

              An appropriate consideration of complex temperature profile and its extreme gradient which are the most prevailing feature of selective laser sintering (SLS)-additive manufacturing (AM) process for simulating the microstructure evolution during the SLS based AM is reported. A team led by Bai-Xiang Xu from Technical University of Darmstadt, Germany, cooperating with Min Yi from Nanjing University of Aeronautics and Astronautics (NUAA), developed a thermodynamically consistent non-isothermal phase-field model to simulate the microstructure evolution during SLS-AM. In order to save the high computation cost, the authors proposed a novel algorithm analogy to minimum coloring problem and manage to simulate a system of 200 grains with grain tracking algorithm using as low as 8 non-conserved order parameters. After applying the model to SLS of the stainless steel 316L powder, the influences of laser power and scan speed on microstructural features (i.e. the porosity, surface morphology, temperature profile, grain geometry, and densification) are successfully identified. Their work provides a phase-field model and the associated numeric scheme which are promising for the large-scale simulation of SLS-AM process.

              Unconventional topological phase transition in non-symmorphic material KHgX (X=As, Sb, Bi)(非對稱材料中的非常規拓撲相變KHgXX = AsSbBi)
              Chin-Shen KuoTay-Rong ChangSu-Yang Xu & Horng-Tay Jeng
              npj Computational Materials 5:65(2019)
              doi:s41524-019-0201-4
              Published online:06 June 2019

              Abstract| Full Text | PDF OPEN

              摘要:傳統的拓撲相變描述了從拓撲平凡到拓撲非平凡態的演化。我們在這項工作中提出了由Dirac無間隙態介導的兩個拓撲非平凡絕緣態之間的非常規拓撲相變體系,源于非對稱型晶體對稱性,不同于傳統的拓撲相變。KHgXX = AsSbBi)族是第一個實驗上實現的拓撲非同態晶體絕緣體(TNCI),其中拓撲表面態以Mobius扭曲連通性爲特征。基于第一性原理計算,我們通過在KHgX上施加外部壓力,提出了從TNCI到狄拉克半金屬(DSM)的拓撲絕緣體-金屬轉變。我們發現在非同態晶體結構中KHgXDSM相具有不尋常的鏡面ChernCm=-3,其在拓撲上不同于傳統的DSM,例如Na3BiCd3As2。此外,我們通過對稱性破壞預測KHgX中的新TNCI相。這個新的TNCI相的拓撲表面狀態顯示鋸齒形連通性,不同于無應力的連通性。我們的研究結果爲理解拓撲表面狀態如何從量子演化提供了全面的研究   

              Abstract:Traditionally topological phase transition describes an evolution from topological trivial to topological nontrivial state. Originated from the non-symmorphic crystalline symmetry, we propose in this work an unconventional topological phase transition scheme between two topological nontrivial insulating states mediated by a Dirac gapless state, differing from the traditional topological phase transition. The KHgX (X=As, Sb, Bi) family is the first experimentally realized topological non-symmorphic crystalline insulator (TNCI), where the topological surface states are characterized by the Mobius-twisted connectivity. Based on first-principles calculations, we present a topological insulator–metal transition from TNCI into a Dirac semimetal (DSM) via applying an external pressure on KHgX. We find an unusual mirror Chern number Cm=-3 for the DSM phase of KHgX in the non-symmorphic crystal structure, which is topologically distinct from the traditional DSM such as Na3Bi and Cd3As2. Furthermore, we predict a new TNCI phase in KHgX via symmetry breaking. The topological surface states in this new TNCI phase display zigzag connectivity, different from the unstressed one. Our results offer a comprehensive study for understanding how the topological surface states evolve from a quantum. 

              Editorial Summary

              Non-symmorphic material KHgX: Unconventional topological phase transition非對稱材料KHgX:非常規拓撲相變

              該研究提出了由Dirac無間隙態介導的兩個拓撲非平凡絕緣態之間的非常規拓撲相變體系,該體系源于非對稱型晶體對稱性,不同于傳統的拓撲相變。來自中國台灣兩所大學的Tay-Rong ChangHorng-Tay Jeng等,基于第一行原理計算提出了非常規拓撲相轉變。KHgXX = AsSbBi)族是第一個實驗上實現的拓撲非同態晶體絕緣體,其中拓撲表面態以莫比烏斯扭曲連接爲特征。他們基于第一原理計算,通過引入兩個新相來使KHgX的拓撲相圖多樣化。通過施加應力,KHgX經曆拓撲絕緣體-金屬的轉變,從拓撲非同態晶體絕緣體相轉變爲Cm = -2DSM相,在非對稱晶體結構中的非平凡鏡ChernCm = -3。通過對稱性破壞,DSM相轉換爲另一個新的拓撲非同態晶體絕緣體相,其中Cm = -3主導著QSH效應。表面能帶的連通性的變化,提供了拓撲相變的直接證明,而且要實現這些預測的新拓撲相,操縱帶隙是其關鍵

              An unconventional topological phase transition scheme between two topological non-trivial insulating states mediated by a Dirac gapless state, originated from non-symmorphic crystalline symmetry, differing from traditional topological phase transitions. A team co-led by Tay-Rong Chang and Horng-Tay Jeng from universities in Taiwan, China, proposed the unconventional topological phase transition based on the first-principles calculations. A KHgX (X = As, Sb, Bi) family which they studied is the first experimentally realized topologically non-homomorphic crystal insulator (TNCI) where the topological surface states are characterized by Mobius-twisted connectivity. Based on the first principles calculations, they diversify the topological phase diagram of KHgX by introducing two new phases. By applying stress, KHgX undergoes a topological insulator-metal transition from the TNCI phase with Cm = -2 into the DSM phase with a non-trivial mirror Chern number Cm = -3 in the non-symmorphic crystal structure. Through symmetry breakong, the DSM phase transforms into another new TNCI phase with Cm = -3 hosting the QSH effect. The change in the connectivity of the surface bands provides a direct justification of the topological phase transition, and manipulating the band gap is the key to realize these predicted new topological phases.

              Tunable ferromagnetic Weyl fermions from a hybrid nodal ring (源于雜化節點環的可調鐵磁外爾費米子)
              Baobing Zheng, Bowen Xia, Rui Wang, Jinzhu Zhao, Zhongjia Chen, Yujun Zhao Hu Xu
              npj Computational Materials 5:74(2019)
              doi:s41524-019-0214-z
              Published online:15 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:近年來,實現非平庸的能帶拓撲結構是凝聚態系統中一個極受關注的熱點。基于第一性原理計算和對稱性分析,本研究報道了在鐵磁半金屬氧化物CrP2O7中的可調外爾費米子的拓撲相。忽略自旋軌道耦合的情況下,CrP2O7能帶中不同類型的節點形成雜化的節點環。考慮自旋軌道耦合的情況下,體系的自旋翻轉對稱性破缺,因此,雜化的節點環縮減爲離散的節點,形成了不同類型的外爾點。該體系投影在(100)面的費米弧清晰可見,有助于在實驗上研究CrP2O7的拓撲性質。此外,計算得到的准粒子幹涉圖樣對實驗研究也很有幫助。本工作提供了一種良好的鐵磁外爾半金屬候選材料,並有望應用于拓撲相關領域   

              Abstract:Realization of nontrivial band topology in condensed matter systems is of great interest in recent years. Using first-principles calculations and symmetry analysis, we propose an exotic topological phase with tunable ferromagnetic Weyl fermions in a half-metallic oxide CrP2O7. In the absence of spin–orbit coupling (SOC), we reveal that CrP2O7 possesses a hybrid nodal ring. When SOC is present, the spin-rotation symmetry is broken. As a result, the hybrid nodal ring shrinks to discrete nodal points and forms different types of Weyl points. The Fermi arcs projected on the (100) surface are clearly visible, which can contribute to the experimental study for the topological properties of CrP2O7. In addition, the calculated quasiparticle interference patterns are also highly desirable for the experimental study of CrP2O7. Our findings provide a good candidate of ferromagnetic Weyl semimetals, and are expected to realize related topological applications with their attracted features. 

              Editorial Summary

              Ferromagnetic Weyl Semimetal CrP2O7: From a hybrid nodal ring to tunable Weyl fermions新型鐵磁外爾半金屬:來自南科大慢悠悠的老虎

              該研究提出了CrP2O7 是一種第一類和第二類外爾點共存的鐵磁外爾半金屬材料,共存的不同類型外爾點來源于沒有自旋軌道耦合時的雜化節點環。來自南方科技大學的徐虎教授領導的團隊(簡稱慢悠悠老虎團),基于第一性原理計算和對稱性分析,研究了鐵磁材料CrP2O7 的拓撲能帶結構。在考虑自旋轨道耦合的情况下,体系的杂化节点环缩减为不同类型的外尔点。通过外加磁场改变磁化方向,可以调节外尔点的數量和类型。此外,计算得到的费米弧和准粒子干涉图样非常有助于实验的进一步观测。該研究结果为深入研究磁性和拓扑之间的相互作用提供了一种理想候选材料,并且加深了人们对铁磁拓扑半金属材料的认识

              CrP2O7 is demonstrated to be a ferromagnetic Weyl semimetal with the coexistence of type-I and type-II Weyl fermions, which originates from a hybrid nodal ring without spin-orbital coupling (SOC). A team led by Hu Xu from the Southern University of Science and Technology, reported the nontrivial band topology of CrP2O7 by using first-principles calculations and symmetry analysis. The hybrid nodal ring of CrP2O7 without SOC shrinks to different types of Weyl points when SOC is included, and the numbers and types of Weyl points can be tuned by external magnetic field. In addition, the calculated Fermi arcs and quasiparticle interference patterns facilitate the experimental study of the topological properties of CrP2O7. Their findings provide a good candidate of studying the interplay between magnetism and topology physics, and deepen the understanding of ferromagnetic Weyl semimetal.

              Transparent conducting materials discovery using high-throughput computing (基于高通量計算發現透明導電材料)
              Guillaume BruninFrancesco RicciViet-Anh HaGian-Marco Rignanese & Geoffroy Hautier
              npj Computational Materials 5:63(2019)
              doi:s41524-019-0200-5
              Published online:04 June 2019

              Abstract| Full Text | PDF OPEN

              摘要:從太陽能電池到透明電子器件的諸多領域都有透明導電材料(TCMs)的應用。由于透明性和導電性的競爭關系,開發兼具高透明性和高導電性的高性能材料,尤其是p型材料,困難很大。最近,高通量從頭算篩選已成爲加速材料發現的強大工具。本綜述討論了如何將這種方法應用于識別TCMs。我們簡要概述了TCMs关键的幾種材料特性(如,可摻雜性、有效質量和透明度),並介紹了可用于評估它們的從頭算技術。我們專注于方法的准確性以及它們對高通量計算的適用性。最後,我們綜述了尋找新TCM的不同高通量計算研究,討論了它們在方法學和主要發現方面的差異   

              Abstract:Transparent conducting materials (TCMs) are required in many applications from solar cells to transparent electronics. Developing high performance materials combining the antagonistic properties of transparency and conductivity has been challenging especially for p-type materials. Recently, high-throughput ab initio computational screening has emerged as a formidable tool for accelerating materials discovery. In this review, we discuss how this approach has been applied for identifying TCMs. We provide a brief overview of the different materials properties of importance for TCMs (e.g., dopability, effective mass, and transparency) and present the ab initio techniques available to assess them. We focus on the accuracy of the methodologies as well as their suitability for high-throughput computing. Finally, we review the different high-throughput computational studies searching for new TCMs and discuss their differences in terms of methodologies and main findings. 

              Editorial Summary

              New transparent conducting materials: discovery by high-throughput computing新型透明導電材料:高通量計算的發現

              透明導電氧化物(TCO)是使用極爲廣泛的材料。任何擁有智能手機的人實際上都會攜帶一層薄薄的TCO,因爲手機觸摸屏就使用了這類材料。這些引人入勝的材料是材料科學家面臨的典型挑戰的傑作,因爲它們結合了相互排斥的特性:透明性和導電性,相關研究方興未艾。來自比利時凝聚態物質和納米科學研究所(IMCN)的Geoffroy  Hautier教授領導的團隊,綜述了透明導電材料所有最新的关键進展。從頭算技術已經達到了非常成熟的水平,以至于理解和預測TCO屬性所需的許多光學、電子和缺陷屬性,現在都可以按理想的精確度和成本來計算獲知。這類方法,特別是其中的高通量方法,被用来执行材料的预测,主要用于處理对pTCM的搜索。雖然還有許多工作要做,但是通過計算發現並通過實驗確認的一些材料(例如,Ba2BiTaO6TaIrGe)已經清楚地展示了令人興奮的初步成功。這些先前的成功使人們確信將來會出現更多對先前高通量計算預測結果的確認。高通量研究還提供了更清晰的未來材料探索方向

              Transparent conducting oxide (TCOs) are widely used materials. Anyone with a smartphone actually carries a thin layer of TCO as these materials are used in touchscreens. These fascinating materials are emblematic of the typical challenges faced by materials scientists as they combine properties that are antagonistic: transparency and conductivity. A team led by Prof. Geoffroy Hautier from the Institut de la Matière Condensée et des Nanosciences (IMCN), Belgium, reviewed all the important progresses in the field of transparent conducting materials (TCMs). Ab initio techniques have reached such a level of maturity that many of the optical, electronic, and defect properties required to understand and predict TCOs properties are nowadays computable with a reasonable accuracy and cost. This led to their use to perform materials prediction especially using high-throughput approaches and mainly tackling the search for p-type TCMs. While there is still much work to be done, exciting first successes have clearly been achieved with a few materials discovered in silico and confirmed experimentally (e.g., Ba2BiTaO6 and TaIrGe). These previous successes make the authors confident that more confirmation of previous computational predictions from high-throughput studies will emerge in the future. The high-throughput studies have also provided a clearer view of the opportunities for future materials exploration.

              Tracking materials science data lineage to manage millions of materials experiments and analyses (跟踪材料科学數据的衍变谱系以管理數百万材料实验和分析)
              Edwin SoedarmadjiHelge S. SteinSantosh K. SuramDan Guevarra & John M. Gregoire
              npj Computational Materials 5:79(2019)
              doi:s41524-019-0216-x
              Published online:26 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:从數据中提取知识的算法,正在飞速发展,數据和元數据管理,越来越成为研究能否取得成果的要素。在材料科学中,包含各种不同类型信息的实验數据库的情况很少见,与其他学科相比,材料數据库规模也相对较小。这些问题的背后是管理和链接全然不同的合成和表征的实验數据,这种管理与连接就是一种挑战。本研究通过开发一种轻量级數据管理框架来解决这些问题,而这个框架通常也适用于实验科学及其他领域。使用该系统进行五年的实验管理,产生了材料实验和分析數据库(MEAD),其中包含来自數百万种材料的合成实验和表征实验的原始數据和元數据,以及由附带的开源存储库中的软件进行數据分析和升华得到的属性數据和性能指标數据。空前庞大的实验數据量和數据多样性,可通过实验和分析的一些属性进行搜索,而这些属性是由研究人员决定的或數据處理软件生成的。搜索网络界面允许用户可视化他们的搜索结果,可下载压缩數据包,并附带其衍变谱系的完整注释。數据的庞大特点为數据科学在物理科学中的应用带来了巨大的挑战和机遇,數据和算法管理框架,MEAD,將促進材料和化學研究的自動化和自主發現二者更充分地結合   

              Abstract:In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research. 

              Editorial Summary

              Tracking data lineage: manage and analyses新型材料实验和分析數据库:跟踪數百万材料數据衍变

              該研究报道了材料实验和分析數据库(MEAD的初始版本。來自美國加州理工學院人工光合作用聯合中心的John M. Gregoire  开发了一种轻量级數据管理框架来解决管理和链接全然不同的合成和表征的实验數据所带来的挑战,并成功建立了MEAD,其中包含了来自數百万种材料的合成实验和表征实验的原始數据和元數据,以及由附带的开源存储库中的软件进行數据分析和升华得到的属性數据和性能指标數据。MEAD为研究人员提供了數百万种材料的合成和材料表征信息,主要是光学和电化学特性信息。通过追踪數据處理中使用的算法,MEAD提供了完整的數据衍变谱系,因此用户可以探索原始數据及其派生属性的解释。Web搜索界面可使用嵌入式DOI检索數据,并下载所需的原始和/或分析數据集。數据和元數据管理旨在吸引快速发展的數据科学领域,为材料实验提供附加值,并促进材料科学中计算机辅助发现新材料的应用

              The initial publication of the Materials Experiment and Analysis Database (MEAD) is reported. A team led by Prof. John M. Gregoire from the California Institute of Technology, USA, developed a lightweight data management framework to overcome the challenges in managing and linking data across disparate synthesis and characterization experiments,, and successfully built MEAD that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. MEAD provides researchers access to information on synthesis and materials characterization, primarily optical and electrochemical properties, for millions of materials. The need to manage data from a diverse set of both custom-built and purchased instruments led to the development of a comprehensive data management system for materials experiments with the requisite flexibility to adapt to the natural evolution of research methods and objectives. With additional tracking of the algorithms used in data processing, MEAD provides the full data lineage so users can explore the raw data and its interpretation that yielded the derived properties. The web search interface enables exploration of data and download of desired raw and/or analyzed data sets with an embedded DOI. The data and metadata management is also intended to engage the rapidly developing field of data science to provide added value to materials experiments and foster the adoption of computer-aided discovery in materials science.

              Semi-supervised machine-learning classification of materials synthesis procedures (材料合成過程的半監督機器學習分類)
              Haoyan HuoZiqin RongOlga KononovaWenhao SunTiago BotariTanjin HeVahe Tshitoyan & Gerbrand Ceder
              npj Computational Materials 5:62(2019)
              doi:s41524-019-0204-1
              Published online:08 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:将海量的科学文献數字化可以为科学分析和综合分析(meta-分析)提供新的信息学方法。然而,科学文献中的大多數内容都是用书面自然语言锁定的,用硬编码分类规则将其解析成數据库十分困难。本研究中,我们展示了一种半监督机器学习方法,用于从书面自然语言中获得无机材料合成步骤并对其进行分类。在没有任何人为输入的情况下,隐含狄利克雷主题模型(Latent Dirichlet Allocation)可以将关键词聚类成与特定实验材料合成步骤相对应的主题,例如“研磨”和“加热”、“溶解”和“离心”等。以适量的注释为指导,随机森林分类器可将这些步骤与不同类别的材料合成相关联,如固态或水热合成。最后,我们表明采用马尔可夫链表示的实验步骤顺序能够精确地重建可能的合成过程流程图。本研究提出的机器学习方法可以批量化地从文献中获得大量无机材料合成信息,并将其處理为标准化的机器可读數据库   

              Abstract:Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as “grinding” and “heating”, “dissolving” and “centrifuging”, etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database. 

              Editorial Summary

              Semi-supervised machine-learning: classification of synthesis procedures大牛Ceder:半監督機器學習閱讀識別、應用自然語言

              該文報道了一種半監督機器學習方法,用來對書面自然語言寫成的無機材料合成步驟進行分類。由美國加州大學伯克利分校的Gerbrand Ceder教授(本刊编委,美国工程院院士)領導的團隊,采用無監督的算法從已發表的220多万篇文献中提取相关材料合成方法及步骤的信息,进而用监督学习方法对这些方案进行分类。通过两种机器学习模式的结合,可以精确获得材料合成的多层次信息,并以人类可读的方式呈现出来。他们的研究表明,上述机器学习方法不仅能对材料合成过程精确分类,而且能够重构出材料的合成路线图。該研究的关键意义在于,创新性地提出了从自然语言书写的文献中,批量提取材料合成的信息用于机器读取,并基于机器学习给出了相应的落实方案,为无机材料合成數据库的构建奠定了关键基础

              A semi-supervised machine-learning algorithms for classification of materials synthesis procedures which is trained on data sets small enough to be manually annotated by individual experts is reported. A team led by Gerbrand Ceder, a member of editorial board of this journal, and an academician of the American Academy of Engineering, from the University of California, Berkeley, USA, extracted and classified the synthesis information of the inorganic materials from the published literatures written in natural language. They firstly, used unsupervised algorithm to extract information about the methods and steps of material synthesis from more than 2.2 million published literatures, and then classified these information by supervised learning method. Through the combination of the two machine learning modes, the multi-level information of material synthesis can be accurately obtained and presented in a human-readable manner. Their research shows that the machine learning method mentioned above can not only classify the synthetic process of materials accurately, but also reconstruct the synthetic route chart of materials. This research innovatively proposes a scalable approach to extract information of materials synthesis from literatures written in natural language to process it into machine-readable database, and lays an important foundation for the construction of inorganic materials synthesis database.

              Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm (機器學習輔助分子設計算法發現高導熱性聚合物)
              Stephen WuYukiko KondoMasa-aki KakimotoBin YangHironao YamadaIsao KuwajimaGuillaume LambardKenta HongoYibin XuJunichiro ShiomiChristoph SchickJunko MorikawaRyo Yoshida
              npj Computational Materials 5:66(2019)
              doi:s41524-019-0203-2
              Published online:21 June 2019

              Abstract| Full Text | PDF OPEN

              摘要:将机器学习应用于计算分子设计,具有加速发现变革性新材料的巨大潜力。然而,其效用在实际应用中,尤其在聚合物科学中,尚未得到证实。本研究展示了基于机器学习辅助聚合物化学成功发现具有高热导率的新聚合物的案例。这一发现是通过机器智能之间的相互作用来落实的,该机器智能可通过聚合物性质的有限數据、实验合成的专业知识、热物理测量的先进技术进行训练。我们训练了分子设计算法,用于此识别热导率及其他目标聚合物性质的定量结构-性能关系,并基于该算法确定了數千种有潜力的候选聚合物。依据可合成性和后续应用的易加工性,我们从这些候选材料中选取了其中的三种,合成其单体并进行了聚合。合成聚合物的热导率达到0.18~0.41 W/mK,其與非複合熱塑性塑料中最好的聚合物相當   

              Abstract:The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics. 

              Editorial Summary

              Discovery of polymers with high thermal conductivity: Machine-learning-assisted molecular design尋找高熱導率聚合物:機器學習輔助的逆向設計

              通过使用一系列机器学习方法,结合聚合物性能综合數据库、有机合成专业技术和热性能先进测量技术,证实了发现新型导热聚合物材料的可行性。来自日本国立物质材料科学研究所(NIMS)的Junko MorikawaRyo Yoshida領導的團隊,基于機器學習輔助聚合物化學設計方法,成功地通過逆向設計發現了一些新型高熱導率聚合物材料。他們基于機器學習模型找到了上千種高熱導率聚合物材料,進一步以可合成性和易加工性做爲依據,最終選擇合成了其中的三種。這些新型聚合物實際測量的熱導率與機器學習預測值高度一致,且性能與非复合热塑性塑料中最优的聚合物相当。該研究提出的這種基于機器學習的逆向設計方法可望進一步推廣用于尋找其他目標性能的新型聚合物

              Discovery of new thermally conductive polymers by the use of a series of machine learning methods in combination with a comprehensive database of polymer properties, expertise from organic synthesis, and advanced measurement technologies for thermal properties is demonstrated. A team co-led by Junko Morikawa and Ryo Yoshida from the National Institute for Materials Science (NIMS), Japan, successful discovers new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. Based on the trained machine learning models, they obtained thousands of candidate polymers by screening. Then three were selected for monomer synthesis and polymerization because of synthetic accessibility and the potential for ease of processing. The measured thermal conductivity of the synthesized polymers is in highly agreement with the predicted values, and is comparable with those of state-of-the-art polymers in non-composite thermo-plastics. The proposed retrosynthesis route assisted by machine learning is promising for discovery novel polymers with other targeted properties.

              Insights into the design of thermoelectric Mg3Sb2 and its analogs by combining theory and experiment (理論結合實驗深入研究Mg3Sb2熱電材料及其類似物的設計)
              Jiawei ZhangLirong Song & Bo Brummerstedt Iversen
              npj Computational Materials 5:76 (2019)
              doi:s41524-019-0215-y
              Published online:17 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:近二十年來,我們見證了開發Mg3Sb2和相關的CaAl2Si2型材料用于低溫和中溫熱電材料的熱潮。本綜述討論了計算與實驗如何結合,爲理解這些材料中的化學鍵、電子輸運、點缺陷、熱輸運和輸運性質各向異性提供見解。在此基礎上,我們研究了設計策略,以指導進一步優化和開發熱電Mg3Sb2基材料及其類似物。我們首先介紹了Zintl相的概念,以理解鍵合和性能,然後針對AMg2X2使用幾乎各向同性的三維化學鍵合網絡,來詳解這一概念。對于電子輸運,我們首先從簡單但非常有用的原子軌道方案設計軌道簡並,並優化p型材料的電性能,進一步在n型體系中討論費米面附近複雜的高能谷簡並、載流子袋各向異性和光導率有效質量等問題,最後討論缺陷控制的載流子濃度與電負性、化學鍵之間的關系。在熱輸運方面,我們討論了Mg3Sb2中出現本征低晶格熱導率的原因。此外,還討論了電子和熱輸運性質的各向異性與晶體軌道和化學鍵的關系。最後,就如何進一步開發此類熱電材料提出了一些具體的挑戰和觀點   

              Abstract:Over the past two decades, we have witnessed a strong interest in developing Mg3Sb2 and related CaAl2Si2-type materials for low- and intermediate-temperature thermoelectric applications. In this review, we discuss how computations coupled with experiments provide insights for understanding chemical bonding, electronic transport, point defects, thermal transport, and transport anisotropy in these materials. Based on the underlying insights, we examine design strategies to guide the further optimization and development of thermoelectric Mg3Sb2-based materials and their analogs. We begin with a general introduction of the Zintl concept for understanding bonding and properties and then reveal the breakdown of this concept in AMg2X2 with a nearly isotropic three-dimensional chemical bonding network. For electronic transport, we start from a simple yet powerful atomic orbital scheme of tuning orbital degeneracy for optimizing p-type electrical properties, then discuss the complex Fermi surface aided by high valley degeneracy, carrier pocket anisotropy, and light conductivity effective mass responsible for the exceptional n-type transport properties, and finally address the defect-controlled carrier density in relation to the electronegativity and bonding character. Regarding thermal transport, we discuss the insight into the origin of the intrinsically low lattice thermal conductivity in Mg3Sb2. Furthermore, the anisotropies in electronic and thermal transport properties are discussed in relation to crystal orbitals and chemical bonding. Finally, some specific challenges and perspectives on how to make further developments are presented. 

              Editorial Summary

              New insights of thermoelectric materials: theory and experiment熱電材料的新熱點:理論與實驗

              本綜述總結了一些成功的指導原則,用于認識熱電材料Mg3Sb2及其衍生物CaAl2Si2型結構的電熱輸運性質。來自丹麥奧胡斯大學化學與iNANO系材料晶體學中心的Bo Brummerstedt Iversen教授和張家偉博士,介紹了許多有啓發性的見解,如軌道交疊、軌道簡並、軌道分裂能、谷退化、有效質量、載流子袋各向異性、費米表面複雜性、點缺陷、電負性、共價鍵,以供深入了解Mg3Sb2及相關CaAl2Si2型熱電材料中的電熱輸運性質。他們從晶體結構和Zintl概念的普通介紹開始,這一概念現已被廣泛應用于理解CaAl2Si2型化合物的結構、鍵合和電子輸運。之後,他們揭示了AMg2X2中幾乎各向同性的3D化學鍵合網絡,其中Zintl公式不再適用。對于p型材料的电子输运行为,他们讨论了如何通过固溶体形成和晶格常數应变来降低晶体轨道分裂能,设计价带顶附近的能带简并,以优化电子输运行为;而对于n型材料,他們揭示了多谷導帶和複雜的費米面是其杰出電熱輸運性能來源。然後,他們回顧了在不同熱力學狀態下,本征p型材料的缺陷化學和令人驚訝的n型材料的輸運特征,突出了缺陷控制的載流子輸運及其與電負性和鍵合特性的相關性。對于熱傳導,作者回顧了Mg3Sb2中固有低晶格熱導率的起源的第一性原理計算研究結果。此外,他們還討論了關于晶體軌道和化學鍵合的電輸運和熱輸運特性的各向異性。最後,作者總結了當前的挑戰和花很大筆墨介紹了未來發展的前景

              Some of the successful guiding principles for understanding and rationalizing the electrical and thermal transport in Mg3Sb2 and its derivatives with the CaAl2Si2-type structure are reviewed. A group led by Prof. Bo Brummerstedt Iversen from the Center for Materials Crystallography, Department of Chemistry and iNANO, Aarhus University, Denmark. reviewed many illuminating insights such as orbital overlap, orbital degeneracy, orbital splitting energy, valley degeneracy, effective mass, carrier pocket anisotropy, Fermi surface complexity, point defects, electronegativity, and bond covalency for understanding electronic and thermal transport of Mg3Sb2 and related CaAl2Si2-type TEs.. They started from general introductions of crystal structure and the Zintl concept that has been widely applied to understand the structure, bonding, and electronic transport in CaAl2Si2-type compounds. After that, they revealed the nearly isotropic three-dimensional (3D) chemical bonding network in AMg2X2, where the Zintl formalism is no longer applicable. For p-type electronic transport, they discussed how electronic transport can be optimized by minimizing the crystal orbital splitting energy via forming solid solutions and tuning biaxial strains, whereas for n-type transport is revealed to be the multi-valley conduction bands and complex Fermi surface as the electronic origin of the extraordinary n-type thermoelectric properties. Then they reviewed the defect chemistry of the intrinsic p-type behavior and the surprising n-type behavior under different thermodynamic states, followed by highlighting the defect-controlled carrier transport and its correlation with the electronegativity and bonding character. For thermal transport, they reviewed the studies on exploring the origin of the intrinsically low lattice thermal conductivity in Mg3Sb2 from first principles calculations. Moreover, they discuss the anisotropy in electrical and thermal transport properties with respect to crystal orbitals and chemical bonding. Finally, they concluded with some current challenges and prospects for future development.

              Conditions for void formation in friction stir welding from machine learning (机器学习研究摩擦搅拌焊接过程中空隙形成条件)
              Yang DuTuhin Mukherjee & Tarasankar DebRoy
              npj Computational Materials 5:68 (2019)
              doi:s41524-019-0207-y
              Published online:09 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:摩擦搅拌焊接接头常含有对其力学性能致命的空隙。本研究使用决策树和贝叶斯神经网络研究了空隙形成的条件。利用摩擦搅拌焊接的解析和數值模型,研究了三种类型的输入數据集,包括未處理的焊接参數和计算变量。本研究分析了AA2024AA2219AA6061等三種鋁合金摩擦攪拌焊接産生空隙的108套独立实验數据。基于神经网络的分析,以焊接参數、试样和刀具几何形状以及材料特征作为输入,预测空隙的形成,精确率为83.3%。利用摩擦攪拌焊接近似分析模型計算潛在致因變量(如溫度、應變速率、扭矩和最大剪切應力)時,分別利用決策樹和神經網絡獲得了90%93.3%的预测精度。当采用严厉的數值模型计算相同的致因变量时,神经网络和决策树均能预测孔洞的形成,其预测精确率为96.6%。在這4個致因變量中,溫度和最大剪切應力對空隙形成的影響最大   

              Abstract:Friction stir welded joints often contain voids that are detrimental to their mechanical properties. Here we investigate the conditions for void formation using a decision tree and a Bayesian neural network. Three types of input data sets including unprocessed welding parameters and computed variables using an analytical and a numerical model of friction stir welding were examined. One hundred and eight sets of independent experimental data on void formation for the friction stir welding of three aluminum alloys, AA2024, AA2219, and AA6061, were analyzed. The neural network-based analysis with welding parameters, specimen and tool geometries, and material properties as input predicted void formation with 83.3% accuracy. When the potential causative variables, i.e., temperature, strain rate, torque, and maximum shear stress on the tool pin were computed from an approximate analytical model of friction stir welding, 90 and 93.3% accuracies of prediction were obtained using the decision tree and the neural network, respectively. When the same causative variables were computed from a rigorous numerical model, both the neural network and the decision tree predicted void formation with 96.6% accuracy. Among these four causative variables, the temperature and maximum shear stress showed the maximum influence on void formation. 

              Editorial Summary

              Machine learning: Conditions for void formation機器學習:摩擦攪拌焊接的空隙形成

              該研究使用两种机器学习算法,即神经网络和决策树算法,研究了铝合金摩擦搅拌中孔洞的形成。来自美国宾夕法尼亚州立大学的Tarasankar DebRoy 領導的研究小組,分析了同行評審文獻中108个独立的实验數据。他们研究了原始焊接参數和潜在的致因变量,如温度、刀具销上的最大剪切应力、扭矩和应变速率等。他们发现1)影響鋁合金攪拌摩擦中孔洞形成的變量依次爲刀具銷附近的溫度、刀具銷上的最大剪應力、扭矩和應變速率,影響程度依次遞減。2)预测孔洞最简单的方法是将原始焊接参數和材料特性输入神经网络,神经网络能够提供一种分类方案,输出二元结果(有孔洞和無孔洞)。該方法對孔隙形成的預測精度爲83.3%3)在预测搅拌摩擦焊接过程中孔洞形成的四个潜在致因变量:温度、工具销上的最大剪应力、扭矩和应变速率均优于原始焊接参數。当这些致因变量由降阶分析模型计算并作为ML算法的输入數据集时,神经网络和DT算法的孔隙形成預測的准確率分別爲93.3%90%4)当空洞形成与潜在的致因变量相关时,即通过建立力学數值模型,计算了针尖附近的局部温度、针尖上的最大剪应力、扭矩和应变速率,结果表明,神经网络和DT方法均能較好地預測缺陷的形成,預測精度爲96.6%

              A family of 2D egg-tray graphenes possessing superior mechanical and electronic properties were constructed by arranging defects in the graphene lattice topologically. The work was co-led by Prof. Maosheng Miao from California State University Northridge, Prof. Haiqing Lin from Beijing Computational Science Research Center, and Prof. Jingyao Liu from Jilin University, and was carried out primarily by Prof. Wei Liu who is currently working in Zhejiang A&F University. They reported that a new family of 2D carbon allotropes (egg-tray graphenes) possessing hillocks and trenches are obtained by arranging pentagons and heptagons in the graphene lattice following varies topological orders. The rational design of these novel graphene allotropes will result in materials with enhanced mechanical strength and a rich variety of electronic properties. Depending on the structure, the egg-tray graphenes can be semi-metallic or semiconducting with either direct or indirect gaps. Some allotropes have Dirac cones in their band structures. Remarkably, some of the allotropes also show negative Poisson’s ratios. This work provides an effective way to improve both the mechanical and electronic properties of graphene. It will enable the rational design of new graphene-based materials with a wide variety of superlative properties.

              Building egg-tray shaped graphenes that have superior mechanical strength and band gap(構建具有優異力學強度和帶隙的蛋盒狀石墨烯
              Wei Liu, Lei Zhao, Eva Zurek, Jing Xia, Yong-hao Zheng, Hai-qing Lin, Jing-yao Liu & Mao-sheng Miao
              npj Computational Materials 5:71 (2019)
              doi:s41524-019-0211-2
              Published online:11 July 2019

              Abstract| Full Text | PDF OPEN

              摘要:石墨烯在二維電子器件領域的應用存在來自于力學和電子兩個方面的障礙。力學方面石墨烯單層傾向于形成褶皺,電子結構方面石墨烯本身缺乏帶隙。此外,石墨烯中不可避免的結構缺陷會對其物理和化學性質産生很大的影響。我們在本研究中通過分析極小點、極大點和鞍點的拓撲分布的規律,發現假如在石墨烯晶格中合理排列五元環和七元環缺陷,可以構建一系列形似盛裝雞蛋的紙板盒一樣的石墨烯。第一性原理計算表明,蛋盒石墨烯是動力學穩定的,其能量依賴于五元環和七元環缺陷的密度,與已知相似的碳同素異形體相比能量較較低。這一族新的二維碳同素異形體電子性質千差萬別。它們有些是半金屬,有些是半導體,還有一些蛋盒石墨烯的能帶結構中具有狄拉克錐結構。一些蛋盒石墨烯還被預測到具有負的泊松比。另外,锂原子在蛋盒石墨烯上的吸附比在石墨烯上的吸附要強得多,因此蛋盒石墨烯可以替換石墨烯,通過更有效地吸附锂原子,來提高可充電锂電池的性能   

              Abstract:The major hindrances of implementing graphene in two-dimensional (2D) electronics are both mechanical (the tendency to crumble and form ripples) and electrical (the lack of a band gap). Moreover, the inevitable structural defects in graphene have a profound influence on its physical and chemical properties. Here, we propose a family of 2D egg-tray graphenes constructed by arranging pentagon and heptagon defects in the graphene lattice based on a careful analysis of the topological distribution of minima, maxima, and saddle points. First-principles calculations show that the egg-tray graphenes are dynamically stable, and their energies, which depend on the concentration of pentagons and heptagons, are the lowest among carbon allotropes. These 2D carbon allotropes exhibit a large variation in their electronic properties, ranging from semimetallic to semiconducting, including some allotropes that have Dirac cones in their band structures. Furthermore, some egg-tray graphenes are predicted to have negative Poisson’s ratios. The adsorption of Li atoms on the egg-tray graphenes is considerably stronger than the adsorption on perfect graphene, therefore they may absorb Li more effectively than graphene, which is important for improving the performance of rechargeable Li batteries. 

              Editorial Summary

              Three pioneers in egg-tray graphenes: Improving mechanical and electronic properties海內外華人三劍客:拿石墨烯做雞蛋包裝盒

              該研究通过在石墨烯晶格中拓扑有序地排列结构缺陷,巧妙地构建了一系列具有优异力学、电子性能的二维蛋盒状石墨烯。来自美国加州州立大学北岭分校的苗茂生教授、北京计算科学研究中心的林海青教授和吉林大学的刘靖尧教授共同合作,目前在浙江农林大学理学院工作的刘伟副教授为该文的第一作者和工作的主要承担人。他们利用第一性原理计算方法,通过在石墨烯晶格中按拓扑顺序排列五元环和七元环结构缺陷,获得了一系列具有起伏结构的二维碳同素异形体,形似超市中用来盛装鸡蛋的纸板盒。随着缺陷的密度和排列方式的不同,蛋盒石墨烯的形状可在很大范围内变化。这些新型石墨烯同素异形体独特的几何形态使得该类材料具有更高的力学强度和各异的电子性能。对应于不同的几何形态,蛋盒石墨烯可以是半金属,也可以是具有直接或间接带隙的半导体。还有一些蛋盒石墨烯的能带结构中具有狄拉克锥结构。值得注意的是,除了提高力学强度外,一些蛋盒石墨烯还显示出负的泊松比,即当蛋盒石墨烯在沿原子层平面内某一方向受压的时候,它在该方向上以及原子层平面内该方向的垂直方向上都会发生收缩。通过缺陷的拓扑有序排列来改善石墨烯力学、电子性能的方法,为有效改善石墨烯力学和电子性能提供了一种新方法,后续研究可依此设计出更多具有优良或独特性能的新型石墨烯基材料

              A family of 2D egg-tray graphenes possessing superior mechanical and electronic properties were constructed by arranging defects in the graphene lattice topologically. The work was co-led by Prof. Maosheng Miao from California State University Northridge, Prof. Haiqing Lin from Beijing Computational Science Research Center, and Prof. Jingyao Liu from Jilin University, and was carried out primarily by Prof. Wei Liu who is currently working in Zhejiang A&F University. They reported that a new family of 2D carbon allotropes (egg-tray graphenes) possessing hillocks and trenches are obtained by arranging pentagons and heptagons in the graphene lattice following varies topological orders. The rational design of these novel graphene allotropes will result in materials with enhanced mechanical strength and a rich variety of electronic properties. Depending on the structure, the egg-tray graphenes can be semi-metallic or semiconducting with either direct or indirect gaps. Some allotropes have Dirac cones in their band structures. Remarkably, some of the allotropes also show negative Poisson’s ratios. This work provides an effective way to improve both the mechanical and electronic properties of graphene. It will enable the rational design of new graphene-based materials with a wide variety of superlative properties.

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